Avionics Checklist Speech Analysis for Configuration Mismatch Alerts
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Solution Overview
Problem
Current aircraft systems lack the ability to identify if a checklist item communicated by one pilot has been misapplied by another pilot, which can lead to catastrophic incidents during critical flight phases.
Innovation Solution
A speech recognition comparison system (SRCS) using artificial intelligence and machine learning models, such as a large language model implemented via a neural network with LSTM blocks, processes pilot input to determine intended aircraft configurations and compares them to current configurations, sending alerts if mismatches are detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pilots use checklists to ensure proper aircraft configuration, then safety and procedural compliance are improved, but the risk of misapplication and human error remains
Solution Approach 1:
The system continuously monitors checklist execution by capturing pilot communications, comparing intended configurations against actual aircraft state, and providing real-time feedback alerts when mismatches are detected. This closed-loop feedback mechanism enables immediate correction of misapplied checklist items before they lead to safety incidents.
Solution Approach 2:
An AI-powered speech recognition and comparison system serves as an intermediary between the pilot and the aircraft systems. This intermediary automatically translates pilot communications into intended configurations, compares them with actual aircraft state, and alerts crew of discrepancies, thereby mediating the human-machine interaction to prevent errors.
2Ease of operation
If manual checklist verification is used, then operational simplicity is maintained, but detection precision of configuration mismatches is insufficient
Solution Approach 1:
The patent replaces manual mechanical verification with an automated AI-based speech recognition and comparison system. This substitution enables precise detection of configuration mismatches through natural language processing and real-time data comparison, while maintaining ease of operation through automated monitoring without requiring additional manual checklist procedures.
3Reliability
If real-time monitoring of checklist execution is implemented, then safety is improved, but system complexity increases
Solution Approach 1:
The system leverages existing multi-functional avionics components and integrated communication systems to perform monitoring functions, rather than adding dedicated complex monitoring hardware. By utilizing available sensors, communication systems, and computational resources for multiple purposes, the solution achieves real-time safety monitoring with minimal additional complexity.
Data Source
AI summary
A system may obtain input data from the pilot input device. A system may process the input data into text. A system may obtain a trained artificial intelligence (AI) and/or machine learning (ML) checklist model. A system may analyze the text via the trained AI and/or ML checklist model, wherein analyzing the text via the trained AI and/or ML checklist model comprises: determining if the text describes a checklist item; and if the text describes the checklist item, determining if the text further describes an intended aircraft configuration based on the checklist item. A system may compare the intended aircraft configuration to a current aircraft configuration. A system may if a mismatch between the intended aircraft configuration and the current aircraft configuration is detected, send an alert signal to the output device.